{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 02数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.makedirs(os.path.join('..', 'data'), exist_ok=True)\n",
    "data_file = os.path.join('..', 'data', 'house_tiny.csv')\n",
    "with open(data_file, 'w') as f:\n",
    "    f.write('NumRooms,Alley,Price\\n')  # 列名\n",
    "    f.write('NA,Pave,127500\\n')  # 每行表示一个数据样本\n",
    "    f.write('2,NA,106000\\n')\n",
    "    f.write('4,NA,178100\\n')\n",
    "    f.write('NA,NA,140000\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   NumRooms Alley   Price\n",
      "0       NaN  Pave  127500\n",
      "1       2.0   NaN  106000\n",
      "2       4.0   NaN  178100\n",
      "3       NaN   NaN  140000\n"
     ]
    }
   ],
   "source": [
    "# 如果没有安装pandas，只需取消对以下行的注释来安装pandas\n",
    "# !pip install pandas\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(data_file)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   NumRooms Alley\n",
      "0       3.0  Pave\n",
      "1       2.0   NaN\n",
      "2       4.0   NaN\n",
      "3       3.0   NaN\n"
     ]
    }
   ],
   "source": [
    "inputs, outputs = data.iloc[:, 0:2], data.iloc[:, 2]\n",
    "inputs = inputs.fillna(inputs.mean())\n",
    "print(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   NumRooms  Alley_Pave  Alley_nan\n",
      "0       3.0           1          0\n",
      "1       2.0           0          1\n",
      "2       4.0           0          1\n",
      "3       3.0           0          1\n"
     ]
    }
   ],
   "source": [
    "inputs = pd.get_dummies(inputs, dummy_na=True)\n",
    "print(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[3., 1., 0.],\n",
       "         [2., 0., 1.],\n",
       "         [4., 0., 1.],\n",
       "         [3., 0., 1.]], dtype=torch.float64),\n",
       " tensor([127500, 106000, 178100, 140000]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "X, y = torch.tensor(inputs.values), torch.tensor(outputs.values)\n",
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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